IDS which are increasingly a key part of system defense are used to identify abnormal activities in a computer system. In general, the traditional intrusion detection relies on the extensive knowledge of security experts, in particular, on their familiarity with the computer system to be protected. To reduce this dependence, various data-mining and machine learning techniques have been used in the literature. Traditional instance-based learning methods can only be used to detect known intrusions, since these methods classify instances based on what they have learned. They rarely detect new intrusions since these intrusion classes has not been able to detect new intrusions as well as known intrusions. In this paper, we propose neural network based method for network intrusion detection. These technique are applied to the KDD Cup 98 data set .In addition, a comparative analysis shows the advantage of Unsupervised Learning techniques over clustering-based Methods in identifying new or unseen attack.

Author Name: Anshuman Sharma and M.R. Alone

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Keywords: Intrusion detection system; neural network; data mining; false alarm.

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EISSN: 2321–8215

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